R: Update the NeRI-based model using new data or new threshold...
updateModel.Res
R Documentation
Update the NeRI-based model using new data or new threshold values
Description
This function will take the frequency-ranked set of variables and will generate a new model with terms that meet the net residual improvement (NeRI) threshold criteria.
The name of the column in data that stores the variable to be predicted by the model
covariates
A string of the type "1 + var1 + var2" that defines which variables will always be included in the models (as covariates)
pvalue
The maximum p-value, associated to the NeRI, allowed for a term in the model
VarFrequencyTable
An array with the ranked frequencies of the features, (e.g. the ranked.var value returned by the ForwardSelection.Model.Res function)
variableList
A data frame with two columns. The first one must have the names of the candidate variables and the other one the description of such variables
data
A data frame where all variables are stored in different columns
type
Fit type: Logistic ("LOGIT"), linear ("LM"), or Cox proportional hazards ("COX")
testType
Type of non-parametric test to be evaluated by the improvedResiduals function: Binomial test ("Binomial"), Wilcoxon rank-sum test ("Wilcox"), Student's t-test ("tStudent"), or F-test ("Ftest")
lastTopVariable
The maximum number of variables to be tested
timeOutcome
The name of the column in data that stores the time to event (needed only for a Cox proportional hazards regression model fitting)
interaction
Set to either 1 for first order models, or to 2 for second order models
maxTrainModelSize
Maximum number of terms that can be included in the model
bootLoops
the number of loops for bootstrap estimation of test error
Value
final.model
An object of class lm, glm, or coxph containing the final model
var.names
A vector with the names of the features that were included in the final model
formula
An object of class formula with the formula used to fit the final model
z.NeRI
A vector in which each element represents the z-score of the NeRI, associated to the testType, for each feature found in the final model
loops
The number of loops it took for the model to stabilize
Author(s)
Jose G. Tamez-Pena and Antonio Martinez-Torteya
See Also
updateModel.Bin
Examples
## Not run:
# Start the graphics device driver to save all plots in a pdf format
pdf(file = "Example.pdf")
# Get the stage C prostate cancer data from the rpart package
library(rpart)
data(stagec)
# Split the stages into several columns
dataCancer <- cbind(stagec[,c(1:3,5:6)],
gleason4 = 1*(stagec[,7] == 4),
gleason5 = 1*(stagec[,7] == 5),
gleason6 = 1*(stagec[,7] == 6),
gleason7 = 1*(stagec[,7] == 7),
gleason8 = 1*(stagec[,7] == 8),
gleason910 = 1*(stagec[,7] >= 9),
eet = 1*(stagec[,4] == 2),
diploid = 1*(stagec[,8] == "diploid"),
tetraploid = 1*(stagec[,8] == "tetraploid"),
notAneuploid = 1-1*(stagec[,8] == "aneuploid"))
# Remove the incomplete cases
dataCancer <- dataCancer[complete.cases(dataCancer),]
# Load a pre-stablished data frame with the names and descriptions of all variables
data(cancerVarNames)
# Rank the variables:
# - Analyzing the raw data
# - Using a Cox proportional hazards fitting
# - According to the NeRI
rankedDataCancer <- univariateRankVariables(variableList = cancerVarNames,
formula = "Surv(pgtime, pgstat) ~ 1",
Outcome = "pgstat",
data = dataCancer,
categorizationType = "Raw",
type = "COX",
rankingTest = "NeRI",
description = "Description")
# Get a Cox proportional hazards model using:
# - 10 bootstrap loops
# - The ranked variables
# - The Wilcoxon rank-sum test as the feature inclusion criterion
cancerModel <- ForwardSelection.Model.Res(loops = 10,
Outcome = "pgstat",
variableList = rankedDataCancer,
data = dataCancer,
type = "COX",
testType= "Wilcox",
timeOutcome = "pgtime")
# Update the model, adding first order interactions
uCancerModel <- updateModel.Res(Outcome = "pgstat",
VarFrequencyTable = cancerModel$ranked.var,
variableList = cancerVarNames,
data = dataCancer,
type = "COX",
testType = "Wilcox",
timeOutcome = "pgtime",
interaction = 2)
# Shut down the graphics device driver
dev.off()
## End(Not run)
providing 
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